Examining the Spatially Non- Stationary Associations Between the Second Demographic Transition and Infant Mortality: A Poisson GWR Approach

Abstract

Based on ecological studies, second demographic transition (SDT) theorists concluded that some areas in the US were in vanguard of the SDT compared to others, implying spatial nonstationarity may be inherent in the SDT process. Linking the SDT to the infant mortality literature, we sought out to answer two related questions: Are the main components of the SDT, specifically marriage postponement, cohabitation, and divorce, associated with infant mortality? If yes, do these associations vary across the US? We applied global Poisson and geographically weighted Poisson regression (GWPR) models, a place-specific analytic approach, to county-level data in the contiguous US. After accounting for the racial/ethnic and socioeconomic compositions of counties and prenatal care utilization, we found (1) marriage postponement was negatively related to infant mortality in the southwestern states, but positively associated with infant mortality in parts of Indiana, Kentucky, and Tennessee, (2) cohabitation rates were positively related to infant mortality, and this relationship was stronger in California, coastal Virginia, and the Carolinas than other areas, and (3) a positive association between divorce rates and infant mortality in southwestern and northeastern areas of the US. These spatial patterns suggested that the associations between the SDT and infant mortality were stronger in the areas in vanguard of the SDT than in others. The comparison between global Poisson and GWPR results indicated that a place-specific spatial analysis not only fit the data better, but also provided insights into understanding the non-stationarity of the associations between the SDT and infant mortality.

Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

“…The GWGR model is simple and commonly used in the related studies about this issue [26]. In order to compare our result to similar studies, and also for convenience consideration, we decided to use GWGR as the basis for our new SD-GTWR model.…”

“One size does not fit all”: Spatial nonstationarity in the determinants of elderly residential isolation in historicalEurope

“…Although demographers have recently begun to recognise spatial contingencies in the factors underlying contemporary fertility variation (Işik & Pinarcioğlu, ; Vitali & Billari, ; Yang, Shoff, & Matthews, ), it remains a riddle whether similar spatial heterogeneity could be identified for earlier periods of human history and in other domains of demographic behaviour. In this paper, we set out to explore this possibility by seeking to answer three interrelated questions: Did the relationships between the share of elderly people living without kin in historic Europe and aggregate contextual factors vary significantly over space?…”

“…This literature provides a strong argument in favour of considering the spatially contingent relationships among the explanatory and dependent variables in demographic research. Several recent papers have picked up this agenda revealing substantial spatial heterogeneity in the association between fertility and its correlates (Işik & Pinarcioğlu, ; Yang et al, ; Vitali & Billari, ). However, a corresponding, spatially sensitive inquiry into the pan‐European diversity of elderly residential patterns and their determinants remains to be undertaken.…”

Parameter estimation of geographically weigthed multivariate Poisson regression

“…Hadayeghi et al [5] showed that the GWPR models are useful for capturing spatially dependent relationships and generally performing better than the conventional Generalized Linear Model. Yang et al [9] applying GWPR to investigated the relationship of marriage postponement, cohabitation rates and divorce rates with infant mortality in the US.…”